where \(R\)  is the IR-SE50 model [1]} that computes face embeddings. Figure REF  shows that PromptGen generates faces of the same ID in different poses, even without being explicitly trained with poses as conditions.
We computed the FID score [2]} of each model using Clean-FID [3]}; following [4]}, we did not use the truncation trick when computing the FID score.
Table REF  shows that PromptGen outperforms existing models in terms of the FID score.
Following [5]}, we then reported the average IR-SE50 [1]} embedding distances for images with the same ID and with different IDs.
Results show that PromptGen achieves the best ID preservation, with a slight sacrifice of ID diversity.
